Fashion & style Creators on Xiaohongshu (RED): 129 Indexed

This is the pool of Fashion & style creators on Xiaohongshu (RED) within Lifestyle — 129 of them. The narrower the niche, the higher the reply rate; searching broadly for "Lifestyle creators" usually spends budget on audiences that do not overlap yours.

How this slice differs from the index as a whole

• Median following is 28,000, 7.1× the index-wide median of 3,946 — expect higher rate cards.
• 84% have recent content captured for judgement versus 45% overall — better-evidenced than average.
• 22% publish an email or IM handle against 6% index-wide — easier to reach directly than average.

How to read these numbers: this covers creators indexed and validated inside Koinon Link, not every creator on the platform. Suspected bought-follower accounts are quarantined and excluded from the table above (shown separately). The authenticity score is our own anomaly detection over follower/engagement structure, not a platform-official metric. Figures update as collection continues.

Creators in this slice

• 东莞外贸工厂老板 (@49314492716) — Ballet flats

• 火锅宝贝 (@2293509531) — Outfit sharing

• 小仙女Melody爱种草 (@104835942) — OOTD outfits

• 柒七外贸 (@63025718013) — Bag sharing and recommendations

• 韦呜呜 (@1053889836) — Plus-size fashion

• 1米58的小胡 种草 (@499409161) — Pinduoduo affordable good finds shop recommendations

• 生哥的外贸好货 (@3850805108) — Clearance sale women's clothing

• 梵珂 (@63033676344) — Small fan polka dot/macaron color design

• 若若美外贸 (@26948275409) — Pure linen pants and shirt recommendations

• 拉菲斯汀种草号 (@1008103768) — Bag recommendations

Ordered by follower tier, first 10 shown. Each link opens that creator’s public profile: what they post about, who reads it, and recent samples — no contact details.

Top 6 countries

Why this niche deserves its own view

"Fashion & style" is defined by who the audience is and what job they do, not by what the creator posts about. Two creators can both cover AI tools while their audiences barely overlap — one talks to media buyers who care about creative and bids, the other to engineers who care about APIs and deployment. Searching by topic mixes them together; searching by audience function does not.